Neural correlates of processing elastic moving faces: A functional near-infrared spectroscopy (fNIRS) study
Bibliographic record
Abstract
Recent behavioral studies showed that moving faces optimize face processing efficiency by facilitating part-based face processing as opposed to static faces (Xiao et al., 2012 & 2013). However, the mechanism of this facial movement facilitation effect is less clear. This study using functional near-inferred spectroscopy (fNIRS) methodology explored the neural mechanisms underlying this moving face effect. Thirty-one adults participated in the current study. The classic Composite Face Effect was used to examine holistic versus part-based face processing. In the dynamic condition, participants first learned a 2-second silent moving face video, depicting chewing and blinking movements in the learning phase. In the testing phase, a static composite face was presented. The test face consisted of upper and lower face halves from different people, displayed either aligned or misaligned. Participants decided whether the upper half was the same person as the one they just learned. The static condition was identical to the dynamic one, except that the learned faces were static pictures. NIRS data were acquired in the temporal and occipital regions. A functional connectivity analysis indicated that learning moving faces led to significantly more positive and negative functional connectivities between brain regions than learning static faces. GLM results revealed a significant greater cortical [deoxy-Hb] response in the middle temporal gyrus for watching moving faces than static faces. In addition, learning moving faces led to decreased [deoxy-Hb] responses to process aligned composite faces (beta = -0.08) but increased responses to misaligned composite faces (beta = 0.11). However, static faces led to similar amount of [deoxy-Hb] activation for aligned (beta = 0.08) and misaligned composite faces (beta = 0.06). This face motion effect in neural activities suggests that facial movement might exert a top-down influence on the primary visual cortex by inhibiting holistic processing when viewing aligned composite faces. Meeting abstract presented at VSS 2015
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".